Cellular community detection for tissue phenotyping in colorectal cancer histology images

Sajid Javed, Arif Mahmood, Muhammad Moazam Fraz, Navid Alemi Koohbanani, Ksenija Benes, Yee Wah Tsang, Katherine Hewitt, David Epstein, David Snead, Nasir Rajpoot

Research output: Contribution to journalArticlepeer-review

89 Scopus citations

Abstract

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping.

Original languageBritish English
Article number101696
JournalMedical Image Analysis
Volume63
DOIs
StatePublished - Jul 2020

Keywords

  • Cellular communities
  • Computational pathology
  • Tissue phenotyping
  • Tumor microenvironment

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